Multi-task learning plays an important role in face multi-attribute prediction. At present, most researches excavate the shared information between attributes by sharing all convolutional layers. However, it is not appropriate to treat the low-level and high-level features of the face multi-attribute equally, because the high-level features are more biased toward the specific content of the category. In this article, a novel multi-attribute tensor correlation neural network (MTCN) is used to predict face attributes. MTCN shares all attribute features at the low-level layers, and then distinguishes each attribute feature at the high-level layers. To better excavate the correlations among high-level attribute features, each sub-network explores useful information from other networks to enhance its original information. Then a tensor canonical correlation analysis method is used to seek the correlations among the highest-level attributes, which enhances the original information of each attribute. After that, these features are mapped into a highly correlated space through the correlation matrix. Finally, we use sufficient experiments to verify the performance of MTCN on the CelebA and LFWA datasets and our MTCN achieves the best performance compared with the latest multi-attribute recognition algorithms under the same settings.

中文翻译：

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一种用于面部属性预测的新型多任务张量相关神经网络

多任务学习在人脸多属性预测中发挥着重要作用。目前，大多数研究通过共享所有卷积层来挖掘属性之间的共享信息。但是，将人脸多属性的低层和高层特征一视同仁是不合适的，因为高层特征更偏向于类别的具体内容。在本文中，一种新颖的多属性张量相关神经网络 (MTCN) 用于预测人脸属性。MTCN 在低层共享所有属性特征，然后在高层区分每个属性特征。为了更好地挖掘高级属性特征之间的相关性，每个子网络都从其他网络中探索有用信息以增强其原始信息。然后使用张量典型相关分析方法寻找最高层属性之间的相关性，增强了每个属性的原始信息。之后，这些特征通过相关矩阵映射到高度相关的空间中。最后，我们使用足够的实验来验证 MTCN 在 CelebA 和 LFWA 数据集上的性能，与相同设置下最新的多属性识别算法相比，我们的 MTCN 实现了最佳性能。